The adjacent cities of El Paso in Texas, USA, and Juarez in Mexico commonly experience pollution episodes, especially during the summer months. In the summer of 2017, successive high and low ozone episodes were observed. Aerosol backscattered data from a laser ceilometer was used to monitor and continuously measure the aerosol-layer characteristics. Meteorological parameters together with the aerosol layer height were used to determine the reason behind these successive high and low ozone events. In our research, both modelling and experimental data of the planetary boundary layer height (PBLH) were obtained and related to atmospheric stability. Aerosol backscatter data was used to investigate the structure, evolution, and influence of the top of the aerosol layer, which is a proxy for PBLHs. A shallow aerosol layer height (1164 ± 59 m) was observed during the high ozone episodes, in contrast to a deep aerosol layer (1990 ± 79 m) during the low ozone episodes. The ozone precursors, the ozone, and the ground-level aerosol concentrations were also examined during these episodes. It was observed that when the ozone was high, the PM_2.5 was high, and when the ozone was low, the PM₁₀ concentrations were high. Analysis of the wind patterns and synoptic scale meteorology also contributed to a better explanation of the nature of these events.

This paper deals with the stochastic analysis of wind speed based on relative humidity data. We propose a stochastic regression technique to estimate the time-varying parameters of wind speed in a semi-arid climate region. The modeling of stochastic parameters of atmospheric data with consistent properties facilitates prediction with higher precision. In order to compare the estimation, we used simulated atmospheric time series and observational time series. The atmospheric time series was generated by the Weather Research and Forecasting (WRF) model, whereas the observational time series was obtained from the surface weather stations. The time-varying parameters of the model used are estimated by Maximum Likelihood process. The results obtained suggest that relative humidity exhibits a stochastic effect to predict stationary wind speed data. This type of analysis helps to characterize some key meteorological variables, which would be useful in forecasting irregular wind speed.